text
stringlengths 0
601k
|
|---|
let tan self = let out__ = CArray . make t 1 in stubs_tan ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tan_ self = let out__ = CArray . make t 1 in stubs_tan_ ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tan_out ~ out self = let out__ = CArray . make t 1 in stubs_tan_out ( CArray . start out__ ) out self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tanh self = let out__ = CArray . make t 1 in stubs_tanh ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tanh_ self = let out__ = CArray . make t 1 in stubs_tanh_ ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tanh_backward ~ grad_output ~ output = let out__ = CArray . make t 1 in stubs_tanh_backward ( CArray . start out__ ) grad_output output ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tanh_backward_grad_input ~ grad_input ~ grad_output ~ output = let out__ = CArray . make t 1 in stubs_tanh_backward_grad_input ( CArray . start out__ ) grad_input grad_output output ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tanh_out ~ out self = let out__ = CArray . make t 1 in stubs_tanh_out ( CArray . start out__ ) out self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tensor_split self ~ sections ~ dim = stubs_tensor_split self ( Int64 . of_int sections ) ( Int64 . of_int dim ) |> to_tensor_list |
let tensor_split_indices self ~ indices ~ dim = stubs_tensor_split_indices self ( List . map Int64 . of_int indices |> CArray . of_list int64_t |> CArray . start ) ( List . length indices ) ( Int64 . of_int dim ) |> to_tensor_list |
let tensor_split_tensor_indices_or_sections self ~ tensor_indices_or_sections ~ dim = stubs_tensor_split_tensor_indices_or_sections self tensor_indices_or_sections ( Int64 . of_int dim ) |> to_tensor_list |
let tensordot self other ~ dims_self ~ dims_other = let out__ = CArray . make t 1 in stubs_tensordot ( CArray . start out__ ) self other ( List . map Int64 . of_int dims_self |> CArray . of_list int64_t |> CArray . start ) ( List . length dims_self ) ( List . map Int64 . of_int dims_other |> CArray . of_list int64_t |> CArray . start ) ( List . length dims_other ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tensordot_out ~ out self other ~ dims_self ~ dims_other = let out__ = CArray . make t 1 in stubs_tensordot_out ( CArray . start out__ ) out self other ( List . map Int64 . of_int dims_self |> CArray . of_list int64_t |> CArray . start ) ( List . length dims_self ) ( List . map Int64 . of_int dims_other |> CArray . of_list int64_t |> CArray . start ) ( List . length dims_other ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let threshold self ~ threshold ~ value = let out__ = CArray . make t 1 in stubs_threshold ( CArray . start out__ ) self threshold value ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let threshold_ self ~ threshold ~ value = let out__ = CArray . make t 1 in stubs_threshold_ ( CArray . start out__ ) self threshold value ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let threshold_backward ~ grad_output self ~ threshold = let out__ = CArray . make t 1 in stubs_threshold_backward ( CArray . start out__ ) grad_output self threshold ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let threshold_backward_grad_input ~ grad_input ~ grad_output self ~ threshold = let out__ = CArray . make t 1 in stubs_threshold_backward_grad_input ( CArray . start out__ ) grad_input grad_output self threshold ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let threshold_out ~ out self ~ threshold ~ value = let out__ = CArray . make t 1 in stubs_threshold_out ( CArray . start out__ ) out self threshold value ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tile self ~ dims = let out__ = CArray . make t 1 in stubs_tile ( CArray . start out__ ) self ( List . map Int64 . of_int dims |> CArray . of_list int64_t |> CArray . start ) ( List . length dims ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let to_ self ~ device = let out__ = CArray . make t 1 in stubs_to_ ( CArray . start out__ ) self ( Device . to_int device ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let to_dense self ~ dtype = let out__ = CArray . make t 1 in stubs_to_dense ( CArray . start out__ ) self ( Kind . packed_to_int dtype ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let to_dense_backward ~ grad input = let out__ = CArray . make t 1 in stubs_to_dense_backward ( CArray . start out__ ) grad input ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let to_device self ~ device ~ dtype ~ non_blocking ~ copy = let out__ = CArray . make t 1 in stubs_to_device ( CArray . start out__ ) self ( Device . to_int device ) ( Kind . packed_to_int dtype ) ( if non_blocking then 1 else 0 ) ( if copy then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let to_dtype self ~ dtype ~ non_blocking ~ copy = let out__ = CArray . make t 1 in stubs_to_dtype ( CArray . start out__ ) self ( Kind . packed_to_int dtype ) ( if non_blocking then 1 else 0 ) ( if copy then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let to_dtype_layout self ~ options ~ non_blocking ~ copy = let out__ = CArray . make t 1 in stubs_to_dtype_layout ( CArray . start out__ ) self ( Kind . packed_to_int ( fst options ) ) ( Device . to_int ( snd options ) ) ( if non_blocking then 1 else 0 ) ( if copy then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let to_mkldnn self ~ dtype = let out__ = CArray . make t 1 in stubs_to_mkldnn ( CArray . start out__ ) self ( Kind . packed_to_int dtype ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let to_mkldnn_backward ~ grad input = let out__ = CArray . make t 1 in stubs_to_mkldnn_backward ( CArray . start out__ ) grad input ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let to_other self other ~ non_blocking ~ copy = let out__ = CArray . make t 1 in stubs_to_other ( CArray . start out__ ) self other ( if non_blocking then 1 else 0 ) ( if copy then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let to_sparse self = let out__ = CArray . make t 1 in stubs_to_sparse ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let to_sparse_sparse_dim self ~ sparse_dim = let out__ = CArray . make t 1 in stubs_to_sparse_sparse_dim ( CArray . start out__ ) self ( Int64 . of_int sparse_dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let topk self ~ k ~ dim ~ largest ~ sorted = let out__ = CArray . make t 2 in stubs_topk ( CArray . start out__ ) self ( Int64 . of_int k ) ( Int64 . of_int dim ) ( if largest then 1 else 0 ) ( if sorted then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let topk_values ~ values ~ indices self ~ k ~ dim ~ largest ~ sorted = let out__ = CArray . make t 2 in stubs_topk_values ( CArray . start out__ ) values indices self ( Int64 . of_int k ) ( Int64 . of_int dim ) ( if largest then 1 else 0 ) ( if sorted then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let totype self ~ scalar_type = let out__ = CArray . make t 1 in stubs_totype ( CArray . start out__ ) self ( Kind . packed_to_int scalar_type ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trace self = let out__ = CArray . make t 1 in stubs_trace ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trace_backward ~ grad ~ sizes = let out__ = CArray . make t 1 in stubs_trace_backward ( CArray . start out__ ) grad ( List . map Int64 . of_int sizes |> CArray . of_list int64_t |> CArray . start ) ( List . length sizes ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let transpose self ~ dim0 ~ dim1 = let out__ = CArray . make t 1 in stubs_transpose ( CArray . start out__ ) self ( Int64 . of_int dim0 ) ( Int64 . of_int dim1 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let transpose_ self ~ dim0 ~ dim1 = let out__ = CArray . make t 1 in stubs_transpose_ ( CArray . start out__ ) self ( Int64 . of_int dim0 ) ( Int64 . of_int dim1 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trapezoid ~ y ~ dim = let out__ = CArray . make t 1 in stubs_trapezoid ( CArray . start out__ ) y ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trapezoid_x ~ y ~ x ~ dim = let out__ = CArray . make t 1 in stubs_trapezoid_x ( CArray . start out__ ) y x ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trapz ~ y ~ x ~ dim = let out__ = CArray . make t 1 in stubs_trapz ( CArray . start out__ ) y x ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trapz_dx ~ y ~ dx ~ dim = let out__ = CArray . make t 1 in stubs_trapz_dx ( CArray . start out__ ) y dx ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let triangular_solve self ~ a ~ upper ~ transpose ~ unitriangular = let out__ = CArray . make t 2 in stubs_triangular_solve ( CArray . start out__ ) self a ( if upper then 1 else 0 ) ( if transpose then 1 else 0 ) ( if unitriangular then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let triangular_solve_x ~ x ~ m self ~ a ~ upper ~ transpose ~ unitriangular = let out__ = CArray . make t 2 in stubs_triangular_solve_x ( CArray . start out__ ) x m self a ( if upper then 1 else 0 ) ( if transpose then 1 else 0 ) ( if unitriangular then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; t0 , t1 |
let tril self ~ diagonal = let out__ = CArray . make t 1 in stubs_tril ( CArray . start out__ ) self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tril_ self ~ diagonal = let out__ = CArray . make t 1 in stubs_tril_ ( CArray . start out__ ) self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tril_indices ~ row ~ col ~ offset ~ options = let out__ = CArray . make t 1 in stubs_tril_indices ( CArray . start out__ ) ( Int64 . of_int row ) ( Int64 . of_int col ) ( Int64 . of_int offset ) ( Kind . packed_to_int ( fst options ) ) ( Device . to_int ( snd options ) ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let tril_out ~ out self ~ diagonal = let out__ = CArray . make t 1 in stubs_tril_out ( CArray . start out__ ) out self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let triplet_margin_loss ~ anchor ~ positive ~ negative ~ margin ~ p ~ eps ~ swap ~ reduction = let out__ = CArray . make t 1 in stubs_triplet_margin_loss ( CArray . start out__ ) anchor positive negative margin p eps ( if swap then 1 else 0 ) ( Reduction . to_int reduction |> Int64 . of_int ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let triu self ~ diagonal = let out__ = CArray . make t 1 in stubs_triu ( CArray . start out__ ) self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let triu_ self ~ diagonal = let out__ = CArray . make t 1 in stubs_triu_ ( CArray . start out__ ) self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let triu_indices ~ row ~ col ~ offset ~ options = let out__ = CArray . make t 1 in stubs_triu_indices ( CArray . start out__ ) ( Int64 . of_int row ) ( Int64 . of_int col ) ( Int64 . of_int offset ) ( Kind . packed_to_int ( fst options ) ) ( Device . to_int ( snd options ) ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let triu_out ~ out self ~ diagonal = let out__ = CArray . make t 1 in stubs_triu_out ( CArray . start out__ ) out self ( Int64 . of_int diagonal ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let true_divide self other = let out__ = CArray . make t 1 in stubs_true_divide ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let true_divide_ self other = let out__ = CArray . make t 1 in stubs_true_divide_ ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let true_divide_out ~ out self other = let out__ = CArray . make t 1 in stubs_true_divide_out ( CArray . start out__ ) out self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let true_divide_scalar self other = let out__ = CArray . make t 1 in stubs_true_divide_scalar ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let true_divide_scalar_ self other = let out__ = CArray . make t 1 in stubs_true_divide_scalar_ ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trunc self = let out__ = CArray . make t 1 in stubs_trunc ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trunc_ self = let out__ = CArray . make t 1 in stubs_trunc_ ( CArray . start out__ ) self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let trunc_out ~ out self = let out__ = CArray . make t 1 in stubs_trunc_out ( CArray . start out__ ) out self ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let type_as self other = let out__ = CArray . make t 1 in stubs_type_as ( CArray . start out__ ) self other ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let unbind self ~ dim = stubs_unbind self ( Int64 . of_int dim ) |> to_tensor_list |
let unflatten self ~ dim ~ sizes = let out__ = CArray . make t 1 in stubs_unflatten ( CArray . start out__ ) self ( Int64 . of_int dim ) ( List . map Int64 . of_int sizes |> CArray . of_list int64_t |> CArray . start ) ( List . length sizes ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let unflatten_dense_tensors ~ flat tensors = stubs_unflatten_dense_tensors flat ( CArray . of_list t tensors |> CArray . start ) ( List . length tensors ) |> to_tensor_list |
let unfold self ~ dimension ~ size ~ step = let out__ = CArray . make t 1 in stubs_unfold ( CArray . start out__ ) self ( Int64 . of_int dimension ) ( Int64 . of_int size ) ( Int64 . of_int step ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let unfold_backward ~ grad_in ~ input_sizes ~ dim ~ size ~ step = let out__ = CArray . make t 1 in stubs_unfold_backward ( CArray . start out__ ) grad_in ( List . map Int64 . of_int input_sizes |> CArray . of_list int64_t |> CArray . start ) ( List . length input_sizes ) ( Int64 . of_int dim ) ( Int64 . of_int size ) ( Int64 . of_int step ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let uniform_ self ~ from ~ to_ = let out__ = CArray . make t 1 in stubs_uniform_ ( CArray . start out__ ) self from to_ ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let unique_consecutive self ~ return_inverse ~ return_counts ~ dim = let out__ = CArray . make t 3 in stubs_unique_consecutive ( CArray . start out__ ) self ( if return_inverse then 1 else 0 ) ( if return_counts then 1 else 0 ) ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; t0 , t1 , t2 |
let unique_dim self ~ dim ~ sorted ~ return_inverse ~ return_counts = let out__ = CArray . make t 3 in stubs_unique_dim ( CArray . start out__ ) self ( Int64 . of_int dim ) ( if sorted then 1 else 0 ) ( if return_inverse then 1 else 0 ) ( if return_counts then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; t0 , t1 , t2 |
let unique_dim_consecutive self ~ dim ~ return_inverse ~ return_counts = let out__ = CArray . make t 3 in stubs_unique_dim_consecutive ( CArray . start out__ ) self ( Int64 . of_int dim ) ( if return_inverse then 1 else 0 ) ( if return_counts then 1 else 0 ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; let t1 = CArray . get out__ 1 in Gc . finalise C . Tensor . free t1 ; let t2 = CArray . get out__ 2 in Gc . finalise C . Tensor . free t2 ; t0 , t1 , t2 |
let unsafe_chunk self ~ chunks ~ dim = stubs_unsafe_chunk self ( Int64 . of_int chunks ) ( Int64 . of_int dim ) |> to_tensor_list |
let unsafe_split self ~ split_size ~ dim = stubs_unsafe_split self ( Int64 . of_int split_size ) ( Int64 . of_int dim ) |> to_tensor_list |
let unsafe_split_with_sizes self ~ split_sizes ~ dim = stubs_unsafe_split_with_sizes self ( List . map Int64 . of_int split_sizes |> CArray . of_list int64_t |> CArray . start ) ( List . length split_sizes ) ( Int64 . of_int dim ) |> to_tensor_list |
let unsqueeze self ~ dim = let out__ = CArray . make t 1 in stubs_unsqueeze ( CArray . start out__ ) self ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let unsqueeze_ self ~ dim = let out__ = CArray . make t 1 in stubs_unsqueeze_ ( CArray . start out__ ) self ( Int64 . of_int dim ) ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bicubic2d self ~ output_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bicubic2d ( CArray . start out__ ) self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( if align_corners then 1 else 0 ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bicubic2d_backward ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bicubic2d_backward ( CArray . start out__ ) grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) ( if align_corners then 1 else 0 ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bicubic2d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bicubic2d_backward_grad_input ( CArray . start out__ ) grad_input grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) ( if align_corners then 1 else 0 ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bicubic2d_out ~ out self ~ output_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bicubic2d_out ( CArray . start out__ ) out self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( if align_corners then 1 else 0 ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bilinear2d self ~ output_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bilinear2d ( CArray . start out__ ) self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( if align_corners then 1 else 0 ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bilinear2d_backward ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bilinear2d_backward ( CArray . start out__ ) grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) ( if align_corners then 1 else 0 ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bilinear2d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bilinear2d_backward_grad_input ( CArray . start out__ ) grad_input grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) ( if align_corners then 1 else 0 ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_bilinear2d_out ~ out self ~ output_size ~ align_corners ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_bilinear2d_out ( CArray . start out__ ) out self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( if align_corners then 1 else 0 ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_linear1d self ~ output_size ~ align_corners ~ scales = let out__ = CArray . make t 1 in stubs_upsample_linear1d ( CArray . start out__ ) self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( if align_corners then 1 else 0 ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_linear1d_backward ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales = let out__ = CArray . make t 1 in stubs_upsample_linear1d_backward ( CArray . start out__ ) grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) ( if align_corners then 1 else 0 ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_linear1d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ align_corners ~ scales = let out__ = CArray . make t 1 in stubs_upsample_linear1d_backward_grad_input ( CArray . start out__ ) grad_input grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) ( if align_corners then 1 else 0 ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_linear1d_out ~ out self ~ output_size ~ align_corners ~ scales = let out__ = CArray . make t 1 in stubs_upsample_linear1d_out ( CArray . start out__ ) out self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( if align_corners then 1 else 0 ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest1d self ~ output_size ~ scales = let out__ = CArray . make t 1 in stubs_upsample_nearest1d ( CArray . start out__ ) self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest1d_backward ~ grad_output ~ output_size ~ input_size ~ scales = let out__ = CArray . make t 1 in stubs_upsample_nearest1d_backward ( CArray . start out__ ) grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest1d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ scales = let out__ = CArray . make t 1 in stubs_upsample_nearest1d_backward_grad_input ( CArray . start out__ ) grad_input grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest1d_out ~ out self ~ output_size ~ scales = let out__ = CArray . make t 1 in stubs_upsample_nearest1d_out ( CArray . start out__ ) out self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest2d self ~ output_size ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest2d ( CArray . start out__ ) self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest2d_backward ~ grad_output ~ output_size ~ input_size ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest2d_backward ( CArray . start out__ ) grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest2d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest2d_backward_grad_input ( CArray . start out__ ) grad_input grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest2d_out ~ out self ~ output_size ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest2d_out ( CArray . start out__ ) out self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest3d self ~ output_size ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest3d ( CArray . start out__ ) self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest3d_backward ~ grad_output ~ output_size ~ input_size ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest3d_backward ( CArray . start out__ ) grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest3d_backward_grad_input ~ grad_input ~ grad_output ~ output_size ~ input_size ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest3d_backward_grad_input ( CArray . start out__ ) grad_input grad_output ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( List . map Int64 . of_int input_size |> CArray . of_list int64_t |> CArray . start ) ( List . length input_size ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_nearest3d_out ~ out self ~ output_size ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_nearest3d_out ( CArray . start out__ ) out self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
let upsample_trilinear3d self ~ output_size ~ align_corners ~ scales_d ~ scales_h ~ scales_w = let out__ = CArray . make t 1 in stubs_upsample_trilinear3d ( CArray . start out__ ) self ( List . map Int64 . of_int output_size |> CArray . of_list int64_t |> CArray . start ) ( List . length output_size ) ( if align_corners then 1 else 0 ) scales_d scales_h scales_w ; let t0 = CArray . get out__ 0 in Gc . finalise C . Tensor . free t0 ; t0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.